Overview

Dataset statistics

Number of variables55
Number of observations15120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 MiB
Average record size in memory482.9 B

Variable types

Numeric11
Categorical44

Alerts

Soil_Type7 has constant value "0"Constant
Soil_Type15 has constant value "0"Constant
Elevation is highly overall correlated with Horizontal_Distance_To_Hydrology and 11 other fieldsHigh correlation
Aspect is highly overall correlated with Hillshade_9am and 2 other fieldsHigh correlation
Slope is highly overall correlated with Hillshade_9am and 2 other fieldsHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Horizontal_Distance_To_HydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with Elevation and 4 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_Noon is highly overall correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 3 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with Elevation and 4 other fieldsHigh correlation
Cover_Type is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Wilderness_Area1 is highly overall correlated with Elevation and 6 other fieldsHigh correlation
Wilderness_Area3 is highly overall correlated with Elevation and 3 other fieldsHigh correlation
Wilderness_Area4 is highly overall correlated with Elevation and 7 other fieldsHigh correlation
Soil_Type3 is highly overall correlated with Wilderness_Area4High correlation
Soil_Type10 is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type29 is highly overall correlated with Wilderness_Area1High correlation
Soil_Type40 is highly overall correlated with ElevationHigh correlation
Wilderness_Area2 is highly overall correlated with ElevationHigh correlation
Soil_Type18 is highly overall correlated with Horizontal_Distance_To_Fire_PointsHigh correlation
Soil_Type30 is highly overall correlated with Wilderness_Area1High correlation
Soil_Type38 is highly overall correlated with ElevationHigh correlation
Soil_Type39 is highly overall correlated with ElevationHigh correlation
Horizontal_Distance_To_Hydrology has 1590 (10.5%) zerosZeros
Vertical_Distance_To_Hydrology has 1890 (12.5%) zerosZeros

Reproduction

Analysis started2023-04-21 07:24:49.166329
Analysis finished2023-04-21 07:26:13.388886
Duration1 minute and 24.22 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

Distinct1665
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3226
Minimum1863
Maximum3849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:13.545117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1863
5-th percentile2117
Q12376
median2752
Q33104
95-th percentile3397
Maximum3849
Range1986
Interquartile range (IQR)728

Descriptive statistics

Standard deviation417.67819
Coefficient of variation (CV)0.1519204
Kurtosis-1.0821158
Mean2749.3226
Median Absolute Deviation (MAD)367
Skewness0.075639707
Sum41569757
Variance174455.07
MonotonicityNot monotonic
2023-04-21T12:56:13.745928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2830 25
 
0.2%
2290 25
 
0.2%
3371 24
 
0.2%
2952 23
 
0.2%
2955 23
 
0.2%
2820 23
 
0.2%
3244 23
 
0.2%
2795 23
 
0.2%
2850 22
 
0.1%
2962 22
 
0.1%
Other values (1655) 14887
98.5%
ValueCountFrequency (%)
1863 1
< 0.1%
1874 1
< 0.1%
1879 1
< 0.1%
1888 1
< 0.1%
1889 2
< 0.1%
1896 1
< 0.1%
1898 1
< 0.1%
1899 1
< 0.1%
1901 1
< 0.1%
1903 2
< 0.1%
ValueCountFrequency (%)
3849 2
< 0.1%
3848 1
< 0.1%
3846 2
< 0.1%
3844 1
< 0.1%
3842 1
< 0.1%
3839 1
< 0.1%
3836 1
< 0.1%
3831 1
< 0.1%
3827 1
< 0.1%
3825 2
< 0.1%

Aspect
Real number (ℝ)

Distinct361
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.67665
Minimum0
Maximum360
Zeros110
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:13.961219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q165
median126
Q3261
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)196

Descriptive statistics

Standard deviation110.0858
Coefficient of variation (CV)0.70263054
Kurtosis-1.1502445
Mean156.67665
Median Absolute Deviation (MAD)77
Skewness0.45093529
Sum2368951
Variance12118.884
MonotonicityNot monotonic
2023-04-21T12:56:14.166024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 117
 
0.8%
0 110
 
0.7%
90 109
 
0.7%
63 89
 
0.6%
76 87
 
0.6%
27 82
 
0.5%
315 81
 
0.5%
75 80
 
0.5%
108 79
 
0.5%
117 78
 
0.5%
Other values (351) 14208
94.0%
ValueCountFrequency (%)
0 110
0.7%
1 48
0.3%
2 50
0.3%
3 54
0.4%
4 51
0.3%
5 46
0.3%
6 57
0.4%
7 48
0.3%
8 56
0.4%
9 51
0.3%
ValueCountFrequency (%)
360 2
 
< 0.1%
359 33
0.2%
358 47
0.3%
357 58
0.4%
356 50
0.3%
355 45
0.3%
354 51
0.3%
353 55
0.4%
352 60
0.4%
351 55
0.4%

Slope
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.501587
Minimum0
Maximum52
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:14.653101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median15
Q322
95-th percentile32
Maximum52
Range52
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.4539268
Coefficient of variation (CV)0.51230991
Kurtosis-0.23831014
Mean16.501587
Median Absolute Deviation (MAD)6
Skewness0.52365834
Sum249504
Variance71.468878
MonotonicityNot monotonic
2023-04-21T12:56:14.899088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 740
 
4.9%
10 739
 
4.9%
13 717
 
4.7%
14 699
 
4.6%
12 677
 
4.5%
15 664
 
4.4%
9 664
 
4.4%
16 640
 
4.2%
17 598
 
4.0%
8 574
 
3.8%
Other values (42) 8408
55.6%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 78
 
0.5%
2 134
 
0.9%
3 210
 
1.4%
4 305
2.0%
5 423
2.8%
6 465
3.1%
7 573
3.8%
8 574
3.8%
9 664
4.4%
ValueCountFrequency (%)
52 1
 
< 0.1%
50 1
 
< 0.1%
49 5
 
< 0.1%
48 1
 
< 0.1%
47 3
 
< 0.1%
46 15
0.1%
45 3
 
< 0.1%
44 5
 
< 0.1%
43 2
 
< 0.1%
42 3
 
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct400
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.1957
Minimum0
Maximum1343
Zeros1590
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:15.094031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167
median180
Q3330
95-th percentile631
Maximum1343
Range1343
Interquartile range (IQR)263

Descriptive statistics

Standard deviation210.0753
Coefficient of variation (CV)0.92464468
Kurtosis2.8039844
Mean227.1957
Median Absolute Deviation (MAD)120
Skewness1.4880525
Sum3435199
Variance44131.63
MonotonicityNot monotonic
2023-04-21T12:56:15.316035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1590
 
10.5%
30 1207
 
8.0%
150 497
 
3.3%
60 490
 
3.2%
42 452
 
3.0%
67 411
 
2.7%
85 381
 
2.5%
108 361
 
2.4%
90 284
 
1.9%
120 283
 
1.9%
Other values (390) 9164
60.6%
ValueCountFrequency (%)
0 1590
10.5%
30 1207
8.0%
42 452
 
3.0%
60 490
 
3.2%
67 411
 
2.7%
85 381
 
2.5%
90 284
 
1.9%
95 259
 
1.7%
108 361
 
2.4%
120 283
 
1.9%
ValueCountFrequency (%)
1343 1
< 0.1%
1318 1
< 0.1%
1294 1
< 0.1%
1261 2
< 0.1%
1260 2
< 0.1%
1218 1
< 0.1%
1213 1
< 0.1%
1208 1
< 0.1%
1203 1
< 0.1%
1201 1
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct423
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.076521
Minimum-146
Maximum554
Zeros1890
Zeros (%)12.5%
Negative1139
Negative (%)7.5%
Memory size236.2 KiB
2023-04-21T12:56:15.532275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-146
5-th percentile-4
Q15
median32
Q379
95-th percentile176
Maximum554
Range700
Interquartile range (IQR)74

Descriptive statistics

Standard deviation61.239406
Coefficient of variation (CV)1.1989737
Kurtosis3.4034987
Mean51.076521
Median Absolute Deviation (MAD)32
Skewness1.5377757
Sum772277
Variance3750.2649
MonotonicityNot monotonic
2023-04-21T12:56:15.754597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1890
 
12.5%
5 217
 
1.4%
3 206
 
1.4%
4 200
 
1.3%
8 198
 
1.3%
7 182
 
1.2%
10 176
 
1.2%
9 166
 
1.1%
2 165
 
1.1%
6 162
 
1.1%
Other values (413) 11558
76.4%
ValueCountFrequency (%)
-146 1
< 0.1%
-134 1
< 0.1%
-123 1
< 0.1%
-115 1
< 0.1%
-114 1
< 0.1%
-110 1
< 0.1%
-108 1
< 0.1%
-104 1
< 0.1%
-103 1
< 0.1%
-100 2
< 0.1%
ValueCountFrequency (%)
554 1
< 0.1%
547 2
< 0.1%
411 1
< 0.1%
403 1
< 0.1%
401 1
< 0.1%
397 2
< 0.1%
395 1
< 0.1%
393 1
< 0.1%
390 1
< 0.1%
387 1
< 0.1%
Distinct3250
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1714.0232
Minimum0
Maximum6890
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:16.009097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile242
Q1764
median1316
Q32270
95-th percentile4635.1
Maximum6890
Range6890
Interquartile range (IQR)1506

Descriptive statistics

Standard deviation1325.0664
Coefficient of variation (CV)0.77307375
Kurtosis1.0224194
Mean1714.0232
Median Absolute Deviation (MAD)690
Skewness1.2478107
Sum25916031
Variance1755800.9
MonotonicityNot monotonic
2023-04-21T12:56:16.196591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 88
 
0.6%
120 56
 
0.4%
390 47
 
0.3%
618 45
 
0.3%
1110 43
 
0.3%
700 41
 
0.3%
108 38
 
0.3%
900 37
 
0.2%
1273 37
 
0.2%
990 37
 
0.2%
Other values (3240) 14651
96.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
30 15
 
0.1%
42 5
 
< 0.1%
60 11
 
0.1%
67 13
 
0.1%
85 10
 
0.1%
90 23
0.2%
95 19
 
0.1%
108 38
0.3%
120 56
0.4%
ValueCountFrequency (%)
6890 1
< 0.1%
6836 1
< 0.1%
6811 1
< 0.1%
6766 1
< 0.1%
6679 1
< 0.1%
6660 1
< 0.1%
6508 2
< 0.1%
6414 1
< 0.1%
6406 1
< 0.1%
6371 1
< 0.1%

Hillshade_9am
Real number (ℝ)

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.7043
Minimum0
Maximum254
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:16.419497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile151
Q1196
median220
Q3235
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)39

Descriptive statistics

Standard deviation30.561287
Coefficient of variation (CV)0.14367969
Kurtosis1.2188105
Mean212.7043
Median Absolute Deviation (MAD)18
Skewness-1.0936806
Sum3216089
Variance933.99226
MonotonicityNot monotonic
2023-04-21T12:56:16.634764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 279
 
1.8%
229 269
 
1.8%
224 265
 
1.8%
228 261
 
1.7%
230 260
 
1.7%
233 248
 
1.6%
223 245
 
1.6%
219 242
 
1.6%
231 239
 
1.6%
225 236
 
1.6%
Other values (166) 12576
83.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
58 1
 
< 0.1%
59 2
< 0.1%
65 1
 
< 0.1%
73 1
 
< 0.1%
78 1
 
< 0.1%
80 2
< 0.1%
81 1
 
< 0.1%
83 3
< 0.1%
85 2
< 0.1%
ValueCountFrequency (%)
254 190
1.3%
253 200
1.3%
252 189
1.2%
251 174
1.2%
250 192
1.3%
249 195
1.3%
248 178
1.2%
247 188
1.2%
246 181
1.2%
245 201
1.3%

Hillshade_Noon
Real number (ℝ)

Distinct141
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.96561
Minimum99
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:16.870579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile175
Q1207
median223
Q3235
95-th percentile250
Maximum254
Range155
Interquartile range (IQR)28

Descriptive statistics

Standard deviation22.801966
Coefficient of variation (CV)0.10413492
Kurtosis1.1534842
Mean218.96561
Median Absolute Deviation (MAD)14
Skewness-0.95323171
Sum3310760
Variance519.92963
MonotonicityNot monotonic
2023-04-21T12:56:17.072710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225 327
 
2.2%
229 324
 
2.1%
226 320
 
2.1%
224 313
 
2.1%
230 311
 
2.1%
223 303
 
2.0%
232 298
 
2.0%
222 297
 
2.0%
228 294
 
1.9%
218 293
 
1.9%
Other values (131) 12040
79.6%
ValueCountFrequency (%)
99 4
< 0.1%
102 1
 
< 0.1%
103 1
 
< 0.1%
107 1
 
< 0.1%
111 2
< 0.1%
113 3
< 0.1%
114 1
 
< 0.1%
115 1
 
< 0.1%
116 1
 
< 0.1%
118 1
 
< 0.1%
ValueCountFrequency (%)
254 133
0.9%
253 163
1.1%
252 152
1.0%
251 183
1.2%
250 167
1.1%
249 176
1.2%
248 196
1.3%
247 210
1.4%
246 214
1.4%
245 207
1.4%

Hillshade_3pm
Real number (ℝ)

Distinct247
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.092
Minimum0
Maximum248
Zeros88
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:17.291451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53
Q1106
median138
Q3167
95-th percentile207
Maximum248
Range248
Interquartile range (IQR)61

Descriptive statistics

Standard deviation45.895189
Coefficient of variation (CV)0.33973285
Kurtosis-0.087343908
Mean135.092
Median Absolute Deviation (MAD)30
Skewness-0.34082723
Sum2042591
Variance2106.3683
MonotonicityNot monotonic
2023-04-21T12:56:17.594956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 182
 
1.2%
149 161
 
1.1%
132 156
 
1.0%
133 154
 
1.0%
142 154
 
1.0%
136 154
 
1.0%
137 152
 
1.0%
138 148
 
1.0%
154 148
 
1.0%
152 145
 
1.0%
Other values (237) 13566
89.7%
ValueCountFrequency (%)
0 88
0.6%
1 1
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
248 2
 
< 0.1%
247 4
< 0.1%
246 4
< 0.1%
245 4
< 0.1%
244 3
< 0.1%
243 4
< 0.1%
242 3
< 0.1%
241 3
< 0.1%
240 7
< 0.1%
239 5
< 0.1%
Distinct2710
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1511.1473
Minimum0
Maximum6993
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:17.881782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile296.9
Q1730
median1256
Q31988.25
95-th percentile3663.05
Maximum6993
Range6993
Interquartile range (IQR)1258.25

Descriptive statistics

Standard deviation1099.9365
Coefficient of variation (CV)0.72788172
Kurtosis3.3854158
Mean1511.1473
Median Absolute Deviation (MAD)595
Skewness1.6170989
Sum22848547
Variance1209860.3
MonotonicityNot monotonic
2023-04-21T12:56:18.156606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 65
 
0.4%
541 51
 
0.3%
636 45
 
0.3%
607 43
 
0.3%
960 42
 
0.3%
573 42
 
0.3%
752 41
 
0.3%
942 40
 
0.3%
242 40
 
0.3%
342 40
 
0.3%
Other values (2700) 14671
97.0%
ValueCountFrequency (%)
0 2
 
< 0.1%
30 9
 
0.1%
42 11
0.1%
60 10
 
0.1%
67 20
0.1%
85 8
 
0.1%
90 9
 
0.1%
95 19
0.1%
108 25
0.2%
120 8
 
0.1%
ValueCountFrequency (%)
6993 1
< 0.1%
6853 1
< 0.1%
6723 1
< 0.1%
6686 1
< 0.1%
6661 1
< 0.1%
6632 1
< 0.1%
6615 1
< 0.1%
6606 1
< 0.1%
6600 1
< 0.1%
6597 1
< 0.1%

Wilderness_Area1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
11523 
1
3597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 11523
76.2%
1 3597
 
23.8%

Length

2023-04-21T12:56:18.341754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:18.547294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11523
76.2%
1 3597
 
23.8%

Most occurring characters

ValueCountFrequency (%)
0 11523
76.2%
1 3597
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11523
76.2%
1 3597
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11523
76.2%
1 3597
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11523
76.2%
1 3597
 
23.8%

Wilderness_Area2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14621 
1
 
499

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14621
96.7%
1 499
 
3.3%

Length

2023-04-21T12:56:18.750169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:18.991022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14621
96.7%
1 499
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 14621
96.7%
1 499
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14621
96.7%
1 499
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14621
96.7%
1 499
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14621
96.7%
1 499
 
3.3%

Wilderness_Area3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
8771 
1
6349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8771
58.0%
1 6349
42.0%

Length

2023-04-21T12:56:19.181901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:19.410760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8771
58.0%
1 6349
42.0%

Most occurring characters

ValueCountFrequency (%)
0 8771
58.0%
1 6349
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8771
58.0%
1 6349
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8771
58.0%
1 6349
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8771
58.0%
1 6349
42.0%

Wilderness_Area4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
10445 
1
4675 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10445
69.1%
1 4675
30.9%

Length

2023-04-21T12:56:19.608636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:19.803514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10445
69.1%
1 4675
30.9%

Most occurring characters

ValueCountFrequency (%)
0 10445
69.1%
1 4675
30.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10445
69.1%
1 4675
30.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10445
69.1%
1 4675
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10445
69.1%
1 4675
30.9%

Soil_Type1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14765 
1
 
355

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14765
97.7%
1 355
 
2.3%

Length

2023-04-21T12:56:19.932755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:20.097836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14765
97.7%
1 355
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 14765
97.7%
1 355
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14765
97.7%
1 355
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14765
97.7%
1 355
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14765
97.7%
1 355
 
2.3%

Soil_Type2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14497 
1
 
623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14497
95.9%
1 623
 
4.1%

Length

2023-04-21T12:56:20.236497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:20.459483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14497
95.9%
1 623
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14497
95.9%
1 623
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14497
95.9%
1 623
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14497
95.9%
1 623
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14497
95.9%
1 623
 
4.1%

Soil_Type3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14158 
1
 
962

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14158
93.6%
1 962
 
6.4%

Length

2023-04-21T12:56:20.641367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:20.821705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14158
93.6%
1 962
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 14158
93.6%
1 962
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14158
93.6%
1 962
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14158
93.6%
1 962
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14158
93.6%
1 962
 
6.4%

Soil_Type4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14277 
1
 
843

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14277
94.4%
1 843
 
5.6%

Length

2023-04-21T12:56:20.962325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:21.119791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14277
94.4%
1 843
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 14277
94.4%
1 843
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14277
94.4%
1 843
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14277
94.4%
1 843
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14277
94.4%
1 843
 
5.6%

Soil_Type5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14955 
1
 
165

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14955
98.9%
1 165
 
1.1%

Length

2023-04-21T12:56:21.266470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:21.466351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14955
98.9%
1 165
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 14955
98.9%
1 165
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14955
98.9%
1 165
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14955
98.9%
1 165
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14955
98.9%
1 165
 
1.1%

Soil_Type6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14470 
1
 
650

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14470
95.7%
1 650
 
4.3%

Length

2023-04-21T12:56:21.609450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:21.779014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14470
95.7%
1 650
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 14470
95.7%
1 650
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14470
95.7%
1 650
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14470
95.7%
1 650
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14470
95.7%
1 650
 
4.3%

Soil_Type7
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15120
100.0%

Length

2023-04-21T12:56:21.923480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:22.064101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15120
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15120
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15120
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15120
100.0%

Soil_Type8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15119 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Length

2023-04-21T12:56:22.204101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:22.357854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Soil_Type9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15110 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Length

2023-04-21T12:56:22.498475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:22.658835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Soil_Type10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
12978 
1
2142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12978
85.8%
1 2142
 
14.2%

Length

2023-04-21T12:56:22.809420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:23.011295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12978
85.8%
1 2142
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 12978
85.8%
1 2142
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12978
85.8%
1 2142
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12978
85.8%
1 2142
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12978
85.8%
1 2142
 
14.2%

Soil_Type11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14714 
1
 
406

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14714
97.3%
1 406
 
2.7%

Length

2023-04-21T12:56:23.201178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:23.382457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14714
97.3%
1 406
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 14714
97.3%
1 406
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14714
97.3%
1 406
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14714
97.3%
1 406
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14714
97.3%
1 406
 
2.7%

Soil_Type12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14893 
1
 
227

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14893
98.5%
1 227
 
1.5%

Length

2023-04-21T12:56:23.528474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:23.720357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14893
98.5%
1 227
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 14893
98.5%
1 227
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14893
98.5%
1 227
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14893
98.5%
1 227
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14893
98.5%
1 227
 
1.5%

Soil_Type13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14644 
1
 
476

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14644
96.9%
1 476
 
3.1%

Length

2023-04-21T12:56:23.884394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:24.073275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14644
96.9%
1 476
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 14644
96.9%
1 476
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14644
96.9%
1 476
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14644
96.9%
1 476
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14644
96.9%
1 476
 
3.1%

Soil_Type14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14951 
1
 
169

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14951
98.9%
1 169
 
1.1%

Length

2023-04-21T12:56:24.245166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:24.490016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14951
98.9%
1 169
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 14951
98.9%
1 169
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14951
98.9%
1 169
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14951
98.9%
1 169
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14951
98.9%
1 169
 
1.1%

Soil_Type15
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15120
100.0%

Length

2023-04-21T12:56:24.671906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:24.856788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15120
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15120
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15120
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15120
100.0%

Soil_Type16
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15006 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15006
99.2%
1 114
 
0.8%

Length

2023-04-21T12:56:25.032677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:25.230554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15006
99.2%
1 114
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 15006
99.2%
1 114
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15006
99.2%
1 114
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15006
99.2%
1 114
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15006
99.2%
1 114
 
0.8%

Soil_Type17
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14508 
1
 
612

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14508
96.0%
1 612
 
4.0%

Length

2023-04-21T12:56:25.430430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:25.654294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14508
96.0%
1 612
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 14508
96.0%
1 612
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14508
96.0%
1 612
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14508
96.0%
1 612
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14508
96.0%
1 612
 
4.0%

Soil_Type18
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15060 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15060
99.6%
1 60
 
0.4%

Length

2023-04-21T12:56:26.271130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:26.454780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15060
99.6%
1 60
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 15060
99.6%
1 60
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15060
99.6%
1 60
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15060
99.6%
1 60
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15060
99.6%
1 60
 
0.4%

Soil_Type19
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15074 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15074
99.7%
1 46
 
0.3%

Length

2023-04-21T12:56:26.605619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:26.801498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15074
99.7%
1 46
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15074
99.7%
1 46
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15074
99.7%
1 46
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15074
99.7%
1 46
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15074
99.7%
1 46
 
0.3%

Soil_Type20
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14981 
1
 
139

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14981
99.1%
1 139
 
0.9%

Length

2023-04-21T12:56:27.015367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:27.248222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14981
99.1%
1 139
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 14981
99.1%
1 139
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14981
99.1%
1 139
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14981
99.1%
1 139
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14981
99.1%
1 139
 
0.9%

Soil_Type21
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15104 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15104
99.9%
1 16
 
0.1%

Length

2023-04-21T12:56:27.435102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:27.663963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15104
99.9%
1 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15104
99.9%
1 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15104
99.9%
1 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15104
99.9%
1 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15104
99.9%
1 16
 
0.1%

Soil_Type22
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14775 
1
 
345

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14775
97.7%
1 345
 
2.3%

Length

2023-04-21T12:56:27.855842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:28.091694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14775
97.7%
1 345
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 14775
97.7%
1 345
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14775
97.7%
1 345
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14775
97.7%
1 345
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14775
97.7%
1 345
 
2.3%

Soil_Type23
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14363 
1
 
757

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14363
95.0%
1 757
 
5.0%

Length

2023-04-21T12:56:28.223214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:28.379459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14363
95.0%
1 757
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 14363
95.0%
1 757
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14363
95.0%
1 757
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14363
95.0%
1 757
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14363
95.0%
1 757
 
5.0%

Soil_Type24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14863 
1
 
257

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14863
98.3%
1 257
 
1.7%

Length

2023-04-21T12:56:28.524272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:28.689576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14863
98.3%
1 257
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14863
98.3%
1 257
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14863
98.3%
1 257
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14863
98.3%
1 257
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14863
98.3%
1 257
 
1.7%

Soil_Type25
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15119 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Length

2023-04-21T12:56:28.849768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:29.010170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Soil_Type26
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15066 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15066
99.6%
1 54
 
0.4%

Length

2023-04-21T12:56:29.156367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:29.319753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15066
99.6%
1 54
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 15066
99.6%
1 54
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15066
99.6%
1 54
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15066
99.6%
1 54
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15066
99.6%
1 54
 
0.4%

Soil_Type27
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15105 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15105
99.9%
1 15
 
0.1%

Length

2023-04-21T12:56:29.520928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:29.695892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15105
99.9%
1 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15105
99.9%
1 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15105
99.9%
1 15
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15105
99.9%
1 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15105
99.9%
1 15
 
0.1%

Soil_Type28
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15111 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15111
99.9%
1 9
 
0.1%

Length

2023-04-21T12:56:29.849795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:30.025686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15111
99.9%
1 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15111
99.9%
1 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15111
99.9%
1 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15111
99.9%
1 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15111
99.9%
1 9
 
0.1%

Soil_Type29
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
13829 
1
 
1291

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 13829
91.5%
1 1291
 
8.5%

Length

2023-04-21T12:56:30.160245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:30.310699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13829
91.5%
1 1291
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 13829
91.5%
1 1291
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13829
91.5%
1 1291
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13829
91.5%
1 1291
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13829
91.5%
1 1291
 
8.5%

Soil_Type30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14395 
1
 
725

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 14395
95.2%
1 725
 
4.8%

Length

2023-04-21T12:56:30.480309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:30.685181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14395
95.2%
1 725
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 14395
95.2%
1 725
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14395
95.2%
1 725
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14395
95.2%
1 725
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14395
95.2%
1 725
 
4.8%

Soil_Type31
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14788 
1
 
332

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Length

2023-04-21T12:56:30.890521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:31.206329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Soil_Type32
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14430 
1
 
690

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14430
95.4%
1 690
 
4.6%

Length

2023-04-21T12:56:31.387215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:31.533280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14430
95.4%
1 690
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 14430
95.4%
1 690
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14430
95.4%
1 690
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14430
95.4%
1 690
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14430
95.4%
1 690
 
4.6%

Soil_Type33
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14504 
1
 
616

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14504
95.9%
1 616
 
4.1%

Length

2023-04-21T12:56:31.669072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:31.878574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14504
95.9%
1 616
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14504
95.9%
1 616
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14504
95.9%
1 616
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14504
95.9%
1 616
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14504
95.9%
1 616
 
4.1%

Soil_Type34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15098 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15098
99.9%
1 22
 
0.1%

Length

2023-04-21T12:56:32.053460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:32.203631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15098
99.9%
1 22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15098
99.9%
1 22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15098
99.9%
1 22
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15098
99.9%
1 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15098
99.9%
1 22
 
0.1%

Soil_Type35
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15018 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15018
99.3%
1 102
 
0.7%

Length

2023-04-21T12:56:32.384564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:32.597432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15018
99.3%
1 102
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 15018
99.3%
1 102
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15018
99.3%
1 102
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15018
99.3%
1 102
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15018
99.3%
1 102
 
0.7%

Soil_Type36
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15110 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Length

2023-04-21T12:56:32.739420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:32.949981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Soil_Type37
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
15086 
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15086
99.8%
1 34
 
0.2%

Length

2023-04-21T12:56:33.116876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:33.337739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15086
99.8%
1 34
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15086
99.8%
1 34
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15086
99.8%
1 34
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15086
99.8%
1 34
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15086
99.8%
1 34
 
0.2%

Soil_Type38
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14392 
1
 
728

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14392
95.2%
1 728
 
4.8%

Length

2023-04-21T12:56:33.534618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:33.735494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14392
95.2%
1 728
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 14392
95.2%
1 728
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14392
95.2%
1 728
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14392
95.2%
1 728
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14392
95.2%
1 728
 
4.8%

Soil_Type39
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14463 
1
 
657

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14463
95.7%
1 657
 
4.3%

Length

2023-04-21T12:56:33.870793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:34.086759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14463
95.7%
1 657
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 14463
95.7%
1 657
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14463
95.7%
1 657
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14463
95.7%
1 657
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14463
95.7%
1 657
 
4.3%

Soil_Type40
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size974.5 KiB
0
14661 
1
 
459

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14661
97.0%
1 459
 
3.0%

Length

2023-04-21T12:56:34.268647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-21T12:56:34.471524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14661
97.0%
1 459
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 14661
97.0%
1 459
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14661
97.0%
1 459
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14661
97.0%
1 459
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14661
97.0%
1 459
 
3.0%

Cover_Type
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.2 KiB
2023-04-21T12:56:34.658406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0000661
Coefficient of variation (CV)0.50001654
Kurtosis-1.2500165
Mean4
Median Absolute Deviation (MAD)2
Skewness0
Sum60480
Variance4.0002646
MonotonicityNot monotonic
2023-04-21T12:56:34.810313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 2160
14.3%
2 2160
14.3%
1 2160
14.3%
7 2160
14.3%
3 2160
14.3%
6 2160
14.3%
4 2160
14.3%
ValueCountFrequency (%)
1 2160
14.3%
2 2160
14.3%
3 2160
14.3%
4 2160
14.3%
5 2160
14.3%
6 2160
14.3%
7 2160
14.3%
ValueCountFrequency (%)
7 2160
14.3%
6 2160
14.3%
5 2160
14.3%
4 2160
14.3%
3 2160
14.3%
2 2160
14.3%
1 2160
14.3%

Interactions

2023-04-21T12:56:08.472033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:43.698297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:48.079465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:50.631655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:52.674406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:55.116210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:57.316273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:59.534338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:01.641589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:04.174270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:06.209034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:08.681310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:45.719626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:48.303326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:50.850521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:52.873455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:55.311173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:57.511009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:59.734859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:01.851940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:04.365479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:06.410270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:08.878717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:45.981332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:48.545175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:51.028434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:53.067467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:55.511057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:57.712750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:59.928371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:02.056342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:04.555620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:06.611457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:09.049884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:46.202189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:48.769037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:51.197886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:53.249921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:55.693225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:57.955602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:00.110026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:02.253776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:04.726057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:06.795890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:09.258757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:46.468024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:49.007887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:51.391101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:53.641577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:55.895420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:58.170381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:00.300993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:02.697927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:04.919499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:07.007654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:09.445184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:46.700881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:49.210541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:51.568657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:53.879425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:56.098477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:58.369001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:00.486350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:02.907775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:05.099288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:07.206381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:09.641622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:46.915576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:49.471376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:51.753954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:54.086564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:56.343740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:58.564452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:00.681207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:03.115655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:05.291703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:07.433593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:09.821897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:47.137789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:49.707229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:51.950218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:54.272576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:56.533922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:58.743763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:00.883274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:03.322914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:05.473019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:07.631042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:10.018999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:47.390630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:49.962072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:52.140109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:54.484643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:56.735471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:58.953298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:01.081355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:03.563766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:05.659990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:07.853472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:10.216636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:47.628483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:50.163949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:52.307272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:54.688516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:56.927249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:59.144089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:01.254615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:03.760950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:05.840644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:08.060345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:10.478474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:47.836615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:50.403796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:52.499759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:54.910254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:57.122247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:55:59.340496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:01.455252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:03.977640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:06.032918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-21T12:56:08.272710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-04-21T12:56:35.061260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-04-21T12:56:36.864460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-04-21T12:56:38.608043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-04-21T12:56:40.421053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-04-21T12:56:42.144006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-04-21T12:56:42.790519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-04-21T12:56:11.022136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-21T12:56:12.111419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
Id
1259651325805102212321486279100000000000000000000000000000001000000000005
22590562212-63902202351516225100000000000000000000000000000001000000000005
3280413992686531802342381356121100000000000000100000000000000000000000000002
427851551824211830902382381226211100000000000000000000000000000000100000000002
52595452153-13912202341506172100000000000000000000000000000001000000000005
625791326300-15672302371406031100000000000000000000000000000001000000000002
7260645727056332222251386256100000000000000000000000000000001000000000005
8260549423475732222301446228100000000000000000000000000000001000000000005
92617459240566662232211336244100000000000000000000000000000001000000000005
1026125910247116362282191246230100000000000000000000000000000001000000000005
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
Id
1511125083326671644204173911385001000000000010000000000000000000000000000006
15112261059176010674231202981328001000000000010000000000000000000000000000006
15113260038251240589212178891261001000000000010000000000000000000000000000006
1511426881041544310805245219991266001000000000001000000000000000000000000000003
15115267010812624247302412251121231001000000000001000000000000000000000000000003
1511626072432325876601702512141282001000010000000000000000000000000000000000003
15117260312119633195618249221911325001000010000000000000000000000000000000000003
15118249213425365117335250220831187001000010000000000000000000000000000000000003
15119248716728218101242229237119932001000010000000000000000000000000000000000003
1512024751973431978270189244164914001001000000000000000000000000000000000000003